Instructions to use ProbeX/Model-J__ResNet__model_idx_0803 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0803 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0803") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0803") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0803") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5cc998fb2f481a5af652c3fe33b139052b484ebc81fbc1078fdbd07c58135ec
- Size of remote file:
- 171 MB
- SHA256:
- 42d10f7ef7dd766300f29b5f6ac1e683392349bb2d8d0c3fff8eb51051091005
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